Approach to managing undiagnosed chest pain: could gastroesophageal reflux disease be the cause?
Bibliographic record
Abstract
OBJECTIVE: To highlight gastroesophageal reflux disease as a common cause of undiagnosed chest pain. SOURCES OF INFORMATION: Diagnostic considerations are based on information in peer-reviewed articles retrieved from MEDLINE. Studies had to be in English and involve at least 30 subjects. Population-based studies had to have a sample size of at least 300 and a response rate of at least 60%. Thirty-seven relevant articles were found. MAIN MESSAGE: Clinical management of patients presenting with diagnostically challenging chest pain starts with a careful search for coronary artery disease and other potentially life-threatening causes. Investigations must continue until the underlying disease is identified and symptoms have been effectively controlled. Ongoing symptoms of undiagnosed chest pain cause considerable suffering, impair quality of life, and add unnecessary costs to the health care system. In more than half the patients with undiagnosed chest pain, symptoms are caused by gastroesophageal disease. Empirical acid-suppressive therapy with a proton pump inhibitor can assist clinicians in identifying patients whose symptoms are acid-related. CONCLUSION: Many patients with undiagnosed chest pain can be managed in primary care, minimizing the need for referrals and costly investigations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".